\subsection{Decision Trees Results}

\begin{table}[h]
\caption{Accuracy of Decision Tree without adaptation}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline 
Application & \%Accuracy & Sensitivity & Specificity & BAC\tabularnewline
\hline
\hline 
Appetency & 98.22 & 0 & 1 & 0.5\tabularnewline
\hline 
Upselling & 94.81 & 0.394 & 0.992 & 0.693\tabularnewline
\hline 
Churn & 92.93 & 0.13 & 0.993 & 0.56\tabularnewline
\hline
\end{tabular}
\label{table:basic}
\end{table}
Table~\ref{table:basic} shows the results of decision tree classifier for appetency, upselling and churn when using all the 230 variables and 50,000 instances with pruning factor i.e.the confidence factor, which controls the extent of pruning set to 0.25. For Appetency, when the pruning was decreased by increasing the confidence factor to 4 as suggested by \cite{chawla}, it further degraded the performance by decreasing the specificity and \% accuracy to 0.4995 and 98.164\% respectively while keeping the sensitivity as 0. It is probably because of the overfitting that got introduced due to lesser pruning.
\begin{table}[h] 
\caption{Decision Tree Results for Appetency using cost matrices}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline 
Cost Ratio & \%Accuracy & Sensitivity & Specificity & BAC\tabularnewline
\hline
\hline 
1:2 & 98.024 & 0.007 & 0.998 & 0.5025\tabularnewline
\hline 
1:3 & 97.216 & 0.016 & 0.989 & 0.5025\tabularnewline
\hline 
1:4 & 97.114 & 0.018 & 0.988 & 0.503\tabularnewline
\hline 
1:6 & 97.008 & 0.019 & 0.987 & 0.503\tabularnewline
\hline
\end{tabular}
\label{table:cost}
\end{table}
Table~\ref{table:cost} shows the results of decision tree classifier for appetency when the cost sensitive classification was done using the cost matrices. It can be seen that as the cost for misclassification of minority classes increases, the sensitivity increases, though not significantly, but the \% accuracy decreases with increase in minority misclassification cost. This shows that there is a tradeoff between \%accuracy and sensitivity, while using cost matrices, in case of skewed datasets.

